def _get_utm(self):
"""
get utm coordinates from lat and lon
"""
self.utm_zone, self.east, self.north = MTutm.LLtoUTM(self._utm_ellipsoid,
self.lat, self.lon)
def latlon2xy(lat, lon, origin):
"""returns position in local rectangular coordinates.
x is positive northwards, y positive eastwards, values given in meter
origin must be given as a 2-tuple/-array in (lat,lon) form
"""
#using existing tool for conversion from lat/lon to northing/easting, then just take planar approximation
#comment: to be checked for overlapping UTM zones!!
dummy1, utm_local_east, utm_local_north = ll2utm.LLtoUTM(23, lat, lon)
dummy2, utm_origin_east, utm_origin_north = ll2utm.LLtoUTM(
23, origin[0], origin[1])
x = utm_local_north - utm_origin_north
y = utm_local_east - utm_origin_east
return x, y
x = np.zeros(line_arr.shape[0])
y = np.zeros(line_arr.shape[0])
z = np.zeros(line_arr.shape[0])
mag = np.zeros(line_arr.shape[0])
#east_0 = 334460.0
#north_0 = 4173370.0
east_0 = 336800.0
north_0 = 4167525.0
for ii, l_arr in enumerate(line_arr[:-1]):
lat = float(l_arr[7])
lon = float(l_arr[8])
depth = float(l_arr[9])
mag[ii] = float(l_arr[10])
zone, east, north = ll2utm.LLtoUTM(23, lat, lon)
x[ii] = (east - east_0) / 1000.
y[ii] = (north - north_0) / 1000.
z[ii] = depth + 3
a2vtk.pointsToVTK(
r"C:\Users\jpeacock\Documents\LV\lv_3d_models\2013_lin_relocated_eq",
y,
x,
z,
data={
'depth': z,
'magnitude': mag
})
dfn = r"c:\Users\jpeacock\Documents\Geothermal\Washington\MB\modem_inv\mb_rot_err03_tip02_cov03_NLCG_016.dat"
save_root = 'mb_rot_m40'
rot_angle = -40.0
#--> read model file
mod_obj = modem.Model()
mod_obj.read_model_file(mfn)
#--> read data file
d_obj = modem.Data()
d_obj.read_data_file(dfn)
#--> get center position
model_center = d_obj.center_position
c_zone, c_east, c_north = utm2ll.LLtoUTM(23, model_center[0], model_center[1])
#--> set padding
east_pad = mod_obj.pad_east + 4
north_pad = mod_obj.pad_north + 4
z_pad = np.where(mod_obj.grid_z > 20000)[0][0]
cos_ang = np.cos(np.deg2rad(rot_angle))
sin_ang = np.sin(np.deg2rad(rot_angle))
rot_matrix = np.matrix(np.array([[cos_ang, sin_ang], [-sin_ang, cos_ang]]))
#--> write model xyz file
lines = []
for kk, zz in enumerate(mod_obj.grid_z[0:z_pad]):
for jj, yy in enumerate(mod_obj.grid_east[east_pad:-east_pad]):
for ii, xx in enumerate(mod_obj.grid_north[north_pad:-north_pad]):
import mtpy.utils.latlongutmconversion as utm2ll
from matplotlib.colorbar import *
from matplotlib.patches import Ellipse
import pickle
#==============================================================================
#==============================================================================
# Inputs
#==============================================================================
plot_type = 'pseudo' #map of pseudo for map or pseudo section
diff_yn = 'y'
line_dir = 'ew'
borehole_loc = (139.72851, -30.2128)
bhz, bhe, bhn = utm2ll.LLtoUTM(23, borehole_loc[1], borehole_loc[0])
#==============================================================================
# a few constants
#==============================================================================
refe = 23
#phase tensor map
ptcmapdict = {
'red': ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 1.0), (1.0, 0.0, 1.0)),
'blue': ((0.0, 0.0, 0.0), (1.0, 0.0, 0.0))
}
ptcmap = LinearSegmentedColormap('ptcmap', ptcmapdict, 256)
#phase tensor map for difference (reverse)
### --> gps Source locations
# Newman 2009
feng_09 = {"lat": 37.678, "lon": -118.930, "depth": 6.0, "name": "Feng_2009"}
# Langbein 1995
lb_95_01 = {"lat": 37.6869, "lon": -118.9150, "depth": 5.5, "name": "langbein_1995"}
lb_95_02 = {"lat": 37.6325, "lon": -118.9353, "depth": 13, "name": "langbein_1995"}
battaglia_03 = {"lat": 37.67856, "lon": -119.0164, "depth": 6, "name": "battaglia_2003"}
mb_15 = {"lat": 37.67852, "lon": -118.914, "depth": 7, "name": "montgomery_brown_2015"}
df = pd.DataFrame()
line_list = []
for gps_dict in [feng_09, lb_95_01, lb_95_02, battaglia_03, mb_15]:
zone, east, north = utm2ll.LLtoUTM(23, gps_dict["lat"], gps_dict["lon"])
gps_east = (east - east_0) / 1000.0
gps_north = (north - north_0) / 1000.0
gps_dict["model_east"] = gps_east
gps_dict["model_north"] = gps_north
line = "{0} center: {1:.2f} E, {2:.2f} N, Depth {3:.1f}".format(
gps_dict["name"], gps_east, gps_north, gps_dict["depth"]
)
if len(line_list) == 0:
line_list.append(",".join([key for key in sorted(gps_dict.keys())]))
line_str = ["{0}".format(gps_dict[key]) for key in sorted(gps_dict.keys())]
line_list.append(",".join(line_str))
print(line)
### --> gps Source locations
# Newman 2009
feng_09 = {'lat':37.678, 'lon':-118.930, 'depth': 6.0, 'name':'Feng_2009'}
# Langbein 1995
lb_95_01 = {'lat':37.6869, 'lon':-118.9150, 'depth':5.5, 'name':'langbein_1995'}
lb_95_02 = {'lat':37.6325, 'lon':-118.9353, 'depth':13, 'name':'langbein_1995'}
battaglia_03 = {'lat':37.67856, 'lon':-119.0164, 'depth':6, 'name':'battaglia_2003'}
mb_15 = {'lat':37.67852, 'lon':-118.914, 'depth':7, 'name':'montgomery_brown_2015'}
df = pd.DataFrame()
line_list = []
for gps_dict in [feng_09, lb_95_01, lb_95_02, battaglia_03, mb_15]:
zone, east, north = utm2ll.LLtoUTM(23, gps_dict['lat'], gps_dict['lon'])
gps_east = (east-east_0)/1000.
gps_north = (north-north_0)/1000.
gps_dict['model_east'] = gps_east
gps_dict['model_north'] = gps_north
line = '{0} center: {1:.2f} E, {2:.2f} N, Depth {3:.1f}'.format(gps_dict['name'],
gps_east, gps_north, gps_dict['depth'])
if len(line_list) == 0:
line_list.append(','.join([key for key in sorted(gps_dict.keys())]))
line_str = ['{0}'.format(gps_dict[key]) for key in sorted(gps_dict.keys())]
line_list.append(','.join(line_str))
print(line)
"""
Created on Thu Mar 20 17:28:04 2014
@author: jpeacock
"""
import os
from evtk.hl import pointsToVTK
import numpy as np
import mtpy.utils.latlongutmconversion as utm2ll
sfn = r"/mnt/hgfs/jpeacock/Documents/MountainPass/Maps/MountainPass_GIS/southern_california_earthquake_locations_crop.txt"
mc_lat = 35.464
mc_lon = -115.472
zone, east_0, north_0 = utm2ll.LLtoUTM(23, mc_lat, mc_lon)
s_array = np.loadtxt(sfn,
delimiter=',',
dtype=[('year', np.float), ('month', np.float),
('day', np.float), ('hour', np.float),
('minute', np.float), ('second', np.float),
('lat', np.float), ('lon', np.float),
('depth', np.float), ('mag', np.float)],
skiprows=1)
east = np.zeros(s_array.shape[0])
north = np.zeros(s_array.shape[0])
for ii, ss in enumerate(s_array):
zz, ee, nn = utm2ll.LLtoUTM(23, ss['lat'], ss['lon'])
s_array = np.loadtxt(sfn, delimiter=',',
dtype = [('year', np.float),
('month', np.float),
('day', np.float),
('lat', np.float),
('lon', np.float),
('depth', np.float),
('mag', np.float)],
skiprows=1)
east = np.zeros(s_array.shape[0])
north = np.zeros(s_array.shape[0])
for ii, ss in enumerate(s_array):
zz, ee, nn = utm2ll.LLtoUTM(23, ss['lat'], ss['lon'])
east[ii] = (ee-east_0)/1000.
north[ii] = (nn-north_0)/1000.
x = north.copy()
y = east.copy()
z = s_array['depth'].copy()
mag = s_array['mag'].copy()
for xx, yy, zz, mm in zip(x, y, z, mag):
print 'N={0:.2f}, E={1:.2f}, Z={2:.2f}, M={3:.2f}'.format(xx, yy, zz, mm)
pointsToVTK(r"/home/jpeacock/Documents/mb_LP_locations", x, y, z,
data={'mag':mag, 'depth':z})
@author: jpeacock-pr
"""
import os
import numpy as np
import mtpy.core.edi as mtedi
import mtpy.core.z as mtz
import mtpy.utils.latlongutmconversion as utmll
data_file = r"C:\Users\jpeacock-pr\Documents\MonoBasin\LV_Wannamaker\LVMTdat.txt"
loc_file = r"c:\Users\jpeacock-pr\Documents\MonoBasin\LV_Wannamaker\LV-LOC.txt"
well_loc = (-118.908611, 37.679722)
save_path = r"c:\Users\jpeacock-pr\Documents\MonoBasin\LV_Wannamaker"
well_zone, well_east, well_north = utmll.LLtoUTM(23, well_loc[1], well_loc[0])
dipole_len = 100
#==============================================================================
# read location file
#==============================================================================
loc_fid = file(loc_file, 'r')
loc_lines = loc_fid.readlines()
ns = int(loc_lines[0].strip())
locations = np.zeros(ns,
dtype=[('name', '|S10'), ('lat', np.float),
('lon', np.float)])
loc_dict = {}
for ii, line in enumerate(loc_lines[1:ns + 1]):
for ii in range(1, int(ny) + 2):
dline = dfid.readline()
if len(str(dline)) > 1:
# needs to be backwards because first line is the furthest north row.
elevation[:, -ii] = np.array(dline.strip().split(" "), dtype="float")
else:
break
dfid.close()
# create lat and lon arrays from the dem fle
lon = np.arange(x0, x0 + cs * (nx), cs)
lat = np.arange(y0, y0 + cs * (ny), cs)
# calculate the lower left and uper right corners of the grid in meters
ll_en = ll2utm.LLtoUTM(23, lat[0], lon[0])
ur_en = ll2utm.LLtoUTM(23, lat[-1], lon[-1])
# estimate cell sizes for each dem measurement
d_east = abs(ll_en[1] - ur_en[1]) / nx
d_north = abs(ll_en[2] - ur_en[2]) / ny
# calculate the number of new cells
num_cells = int(cell_size / np.mean([d_east, d_north]))
# make easting and northing arrays in meters corresponding to lat and lon
east = np.arange(ll_en[1], ur_en[1], d_east)
north = np.arange(ll_en[2], ur_en[2], d_north)
# resample the data accordingly
new_east = east[np.arange(0, east.shape[0], num_cells)]
# model center
east_0 = 555703. - 253
north_0 = 5132626.0 + 26
with open(msh_eq_fn, 'r') as fid:
lines = fid.readlines()
eq_arr = np.zeros(len(lines) - 1,
dtype=([('depth', np.float), ('east', np.float),
('north', np.float), ('mag', np.float)]))
eq_df = pd.read_csv(msh_eq_fn)
for ii in range(eq_df.shape[0]):
eq = eq_df.iloc[ii]
zone, east, north = utm2ll.LLtoUTM(23, eq.LATITUDE_D, eq.LONGITUDE_)
eq_arr[ii]['east'] = (east - east_0) / 1000
eq_arr[ii]['north'] = (north - north_0) / 1000
eq_arr[ii]['depth'] = eq.EARTHQUA_1
eq_arr[ii]['mag'] = eq.EARTHQUA_2
pointsToVTK(msh_eq_fn[:-4],
eq_arr['north'],
eq_arr['east'],
eq_arr['depth'],
data={
'depth': eq_arr['depth'],
'magnitude': eq_arr['mag']
})
model_vel = np.zeros((n_north, n_east, n_z))
model_vel_change = np.zeros((n_north, n_east, n_z))
# lin has west as positive and east as negative
for v_arr in vel_arr:
e_index = east_index_dict[v_arr["east"] * -1]
n_index = north_index_dict[v_arr["north"]]
z_index = z_index_dict[v_arr["depth"]]
v_avg = vel_arr[np.where(vel_arr[:]["depth"] == v_arr["depth"])]["Vp"].mean()
model_vel[n_index, e_index, z_index] = v_arr["Vp"]
model_vel_change[n_index, e_index, z_index] = 100 * ((v_arr["Vp"] - v_avg) / v_avg)
# find the shift between the center of each grid
v_zone, v_east, v_north = ll2utm.LLtoUTM(
23, vel_arr["lat"].mean(), vel_arr["lon"].mean()
)
shift_east = (v_east - mt_east) / 1000.0
shift_north = (v_north - mt_north) / 1000.0
# vel.reshape()
a2vtk.gridToVTK(
os.path.join(os.path.dirname(vp_fn), "lv_3d_models", "lin2015_vp_lvc"),
north + shift_north,
east + shift_east,
z + 2,
cellData={"Vp": model_vel, "Vp_change": model_vel_change},
)
# a2vtk.gridToVTK(os.path.join(os.path.dirname(vp_fn), 'lin2015_vp_change'),
# north+shift_north, east+shift_east, z,
"""
Created on Thu Mar 20 17:28:04 2014
@author: jpeacock
"""
import os
from evtk.hl import pointsToVTK
import numpy as np
import mtpy.utils.latlongutmconversion as utm2ll
sfn = r"/mnt/hgfs/jpeacock/Documents/MountainPass/Maps/MountainPass_GIS/southern_california_earthquake_locations_crop.txt"
mc_lat = 35.464
mc_lon = -115.472
zone, east_0, north_0 = utm2ll.LLtoUTM(23, mc_lat, mc_lon)
s_array = np.loadtxt(
sfn,
delimiter=",",
dtype=[
("year", np.float),
("month", np.float),
("day", np.float),
("hour", np.float),
("minute", np.float),
("second", np.float),
("lat", np.float),
("lon", np.float),
("depth", np.float),
("mag", np.float),
("year", np.float),
("month", np.float),
("day", np.float),
("lat", np.float),
("lon", np.float),
("depth", np.float),
("mag", np.float),
],
skiprows=1,
)
east = np.zeros(s_array.shape[0])
north = np.zeros(s_array.shape[0])
for ii, ss in enumerate(s_array):
zz, ee, nn = utm2ll.LLtoUTM(23, ss["lat"], ss["lon"])
east[ii] = (ee - east_0) / 1000.0
north[ii] = (nn - north_0) / 1000.0
x = north.copy()
y = east.copy()
z = s_array["depth"].copy()
mag = s_array["mag"].copy()
for xx, yy, zz, mm in zip(x, y, z, mag):
print "N={0:.2f}, E={1:.2f}, Z={2:.2f}, M={3:.2f}".format(xx, yy, zz, mm)
pointsToVTK(
r"/home/jpeacock/Documents/mb_LP_locations", x, y, z, data={"mag": mag, "depth": z}
loc_list = line.replace("w", " ").replace("n", " ").strip().split()[4:]
loc_list = [float(jj) for jj in loc_list]
eq_arr[ii][loc_header[0]] = loc_list[0] + loc_list[1] / 60.0
eq_arr[ii][loc_header[1]] = loc_list[2] + loc_list[3] / 60.0
eq_arr[ii][loc_header[2]] = loc_list[4]
eq_arr[ii][loc_header[3]] = loc_list[5]
eq_arr[ii][loc_header[4]] = loc_list[6]
eq_arr[ii][loc_header[5]] = loc_list[7]
eq_arr[ii][loc_header[6]] = loc_list[8]
eq_arr[ii][loc_header[7]] = loc_list[9]
eq_arr[ii][loc_header[8]] = loc_list[10]
##-> write vtk files
# figure out shift in model centers
# mt_zone, mt_east, mt_north = ll2utm.LLtoUTM(23, modem_center[0], modem_center[1])
v_zone, v_east, v_north = ll2utm.LLtoUTM(23, v_lat, v_lon - 0.29)
shift_east = (mt_east - v_east + 4000) / 1000.0
shift_north = (mt_north - v_north + 1000) / 1000.0
# vtk_grid_east = np.append(grid_east, grid_east[-1]*1.1)-shift_east
# vtk_grid_north = np.append(grid_north, grid_north[-1]*1.1)-shift_north
vtk_grid_east = grid_east[1:-1] - shift_east
vtk_grid_north = grid_north[1:-1] - shift_north
vtk_grid_east = np.append(vtk_grid_east, vtk_grid_east[-1] * 1.1)
vtk_grid_north = np.append(vtk_grid_north, vtk_grid_north[-1] * 1.1)
vtk_gz = grid_z[2:-1] + 2
# vtk_vp = np.rot90(f_vp, 3)
vtk_vp = np.flipud(np.fliplr(np.rot90(f_vp[1:-1, 1:-1, :], 1)))
vtk_vs = np.flipud(np.fliplr(np.rot90(f_vs[1:-1, 1:-1, :], 1)))
lines = sfid.readlines()
x = np.zeros(len_file)
y = np.zeros(len_file)
z = np.zeros(len_file)
read_line = 0
east_0 = 336800.00
north_0 = 4167510.00
for sline in lines[14:]:
slst = sline.strip().split()
lat = float(slst[6])
lon = float(slst[7])
depth = float(slst[8])
zone, east, north = utm2ll.LLtoUTM(23, lat, lon)
x[read_line] = (east-east_0)/1000.
y[read_line] = (north-north_0)/1000.
z[read_line] = depth
read_line += 1
sfid.close()
pointsToVTK(r"c:\Users\jpeacock\Documents\LV\lv_3d_models\shelly_2015_mm_eq_lcv", y, x, z,
data={'depth':z})
model_vel = np.zeros((n_north, n_east, n_z))
model_vel_change = np.zeros((n_north, n_east, n_z))
# lin has west as positive and east as negative
for v_arr in vel_arr:
e_index = east_index_dict[v_arr['east'] * -1]
n_index = north_index_dict[v_arr['north']]
z_index = z_index_dict[v_arr['depth']]
v_avg = vel_arr[np.where(
vel_arr[:]['depth'] == v_arr['depth'])]['Vp'].mean()
model_vel[n_index, e_index, z_index] = v_arr['Vp']
model_vel_change[n_index, e_index,
z_index] = 100 * ((v_arr['Vp'] - v_avg) / v_avg)
# find the shift between the center of each grid
v_zone, v_east, v_north = ll2utm.LLtoUTM(23, vel_arr['lat'].mean(),
vel_arr['lon'].mean())
shift_east = (v_east - mt_east) / 1000.
shift_north = (v_north - mt_north) / 1000.
#vel.reshape()
a2vtk.gridToVTK(os.path.join(os.path.dirname(vp_fn), 'lv_3d_models',
'lin2015_vp_lvc'),
north + shift_north,
east + shift_east,
z + 2,
cellData={
'Vp': model_vel,
'Vp_change': model_vel_change
})
#a2vtk.gridToVTK(os.path.join(os.path.dirname(vp_fn), 'lin2015_vp_change'),
